Кто читает:: Кафедра проблем безопасности (Институт проблем безопасности)

Статус:: Курс по выбору

Когда читается:: 1-й курс, 4 модуль

Abstract

After taking this course, you will be able to: - Utilize various Application Programming Interface (API) services to collect data from different social media sources such as YouTube, Twitter, and Flickr. - Process the collected data - primarily structured - using methods involving correlation, regression, and classification to derive insights about the sources and people who generated that data. - Analyze unstructured data - primarily textual comments - for sentiments expressed in them. - Use different tools for collecting, analyzing, and exploring social media data for research and development purposes.

Learning Objectives

collect, process, analyze and explore social media data (primarily structured) for research and development purposes

Expected Learning Outcomes

to define the difference between structured and unstructured data
to collect data from Twitter and YouTube
to conduct correlation and regression data analysis
to visualize the data from various social media services
to analyze unstructured data using Python and R

Course Contents

Introduction to Data Analytics
In this first unit of the course, several concepts related to social media data and data analytics are introduced. We start by first discussing two kinds of data - structured and unstructured. Then look at how structured data, the primary focus of this course, is analyzed and what one could gain by doing such analysis. Finally, we briefly cover some of the visualizations for exploring and presenting data.Make sure to go through the material for this unit in the sequence it's provided. First, watch the four short videos, then take the practice test, followed by the two quizzes. Finally, read the documents about installation and configuration of Python and R. This is very important - before proceeding to the next units, make sure you have installed necessary tools, and also learned how to install new packages/libraries for them. The course expects students to have programming experience in Python and R.
Collecting and Extracting Social Media Data
In this unit we will see how to collect data from Twitter and YouTube. The unit will start with an introduction to Python programming. Then we will use a Python script, with a little editing, to extract data from Twitter. A similar exercise will then be done with YouTube. In both the cases, we will also see how to create developer accounts and what information to obtain to use the data collection APIs. Once again, make sure to go item-by-item in the order provided. Before beginning this unit, ensure that you have all the right tools (Python, R, Anaconda) ready and configured. The lessons depend on them and also your ability to install required packages.
Data Analysis, Visualization, and Exploration
In this unit, we will focus on analyzing and visualizing the data from various social media services. We will first use the data collected before from YouTube to do various statistics analyses such as correlation and regression. We will then introduce R - a platform for doing statistical analysis. Using R, then we will analyze a much larger dataset obtained from Yelp. Make sure you have covered the material in the previous units before proceeding with this. That means, having all the tools (Anaconda, Python, and R) as well as various packages installed. We will also need new packages this time, so make sure you know how to install them to your Python or R. If needed, please review some basic concepts in statistics - specifically, correlation and regression - before or during working on this unit.
Case Studies
In the final unit of this course, we will work on two case studies - both using Twitter and focusing on unstructured data (in this case, text). The first case study will involve doing sentiment analysis with Python. The second case study will take us through basic text mining application using R. We wrap up the unit with a conclusion of what we did in this course and where to go next for further learning and exploration.

Assessment Elements

Quiz 1
Quiz 2
Python Programming Exercise
Twitter data download using Python
YouTube data download using Python
Statistical Analysis with Twitter Data
Data Visualization using R
Sentiment Analysis with Twitter
Text Mining with Twitter

Interim Assessment

Interim assessment (4 module)
0.2 * Data Visualization using R + 0.07 * Python Programming Exercise + 0.05 * Quiz 1 + 0.05 * Quiz 2 + 0.08 * Sentiment Analysis with Twitter + 0.07 * Statistical Analysis with Twitter Data + 0.2 * Text Mining with Twitter + 0.2 * Twitter data download using Python + 0.08 * YouTube data download using Python

Bibliography

Recommended Core Bibliography

Patone, M., & Zhang, L.-C. (2019). On two existing approaches to statistical analysis of social media data. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsarx&AN=edsarx.1905.00635

Recommended Additional Bibliography

Ria Andryani, Edi Surya Negara, & Dendi Triadi. (2019). Social Media Analytics: Data Utilization of Social Media for Research. Journal of Information Systems and Informatics, (2), 193. https://doi.org/10.33557/journalisi.v1i2.23
Szabó, G., & Boykin, O. (2019). Social Media Data Mining and Analytics. Hoboken: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1899346

Магистерская программа «Аналитик деловой разведки»

Контакты

Social Media Data Analytics

Course Syllabus